17,314 research outputs found
Predictive maintenance for the heated hold-up tank
We present a numerical method to compute an optimal maintenance date for the
test case of the heated hold-up tank. The system consists of a tank containing
a fluid whose level is controlled by three components: two inlet pumps and one
outlet valve. A thermal power source heats up the fluid. The failure rates of
the components depends on the temperature, the position of the three components
monitors the liquid level in the tank and the liquid level determines the
temperature. Therefore, this system can be modeled by a hybrid process where
the discrete (components) and continuous (level, temperature) parts interact in
a closed loop. We model the system by a piecewise deterministic Markov process,
propose and implement a numerical method to compute the optimal maintenance
date to repair the components before the total failure of the system.Comment: arXiv admin note: text overlap with arXiv:1101.174
Explainable Predictive Maintenance
Explainable Artificial Intelligence (XAI) fills the role of a critical
interface fostering interactions between sophisticated intelligent systems and
diverse individuals, including data scientists, domain experts, end-users, and
more. It aids in deciphering the intricate internal mechanisms of ``black box''
Machine Learning (ML), rendering the reasons behind their decisions more
understandable. However, current research in XAI primarily focuses on two
aspects; ways to facilitate user trust, or to debug and refine the ML model.
The majority of it falls short of recognising the diverse types of explanations
needed in broader contexts, as different users and varied application areas
necessitate solutions tailored to their specific needs.
One such domain is Predictive Maintenance (PdM), an exploding area of
research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights
the gap between existing XAI methodologies and the specific requirements for
explanations within industrial applications, particularly the Predictive
Maintenance field. Despite explainability's crucial role, this subject remains
a relatively under-explored area, making this paper a pioneering attempt to
bring relevant challenges to the research community's attention. We provide an
overview of predictive maintenance tasks and accentuate the need and varying
purposes for corresponding explanations. We then list and describe XAI
techniques commonly employed in the literature, discussing their suitability
for PdM tasks. Finally, to make the ideas and claims more concrete, we
demonstrate XAI applied in four specific industrial use cases: commercial
vehicles, metro trains, steel plants, and wind farms, spotlighting areas
requiring further research.Comment: 51 pages, 9 figure
Predictive Maintenance on the Machining Process and Machine Tool
This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces
Machine Learning Applications for Predictive Maintenance in Mechanical Systems: Case Studies, Algorithms, and Performance Evaluation
Predictive maintenance is a critical aspect of ensuring the reliability and efficiency of mechanical systems in various industries. Machine learning (ML) techniques have emerged as powerful tools for predictive maintenance, enabling early detection of equipment failures and facilitating timely interventions to prevent costly downtime and repairs. This paper provides an overview of machine learning applications for predictive maintenance in mechanical systems, presenting case studies, algorithms, and performance evaluation metrics. We discuss the significance of predictive maintenance in enhancing operational efficiency, reducing maintenance costs, and minimizing unplanned downtime. Furthermore, we review various machine learning algorithms commonly employed for predictive maintenance, including supervised and unsupervised learning techniques, deep learning models, and ensemble methods. Additionally, we delve into real-world case studies that highlight the successful implementation of machine learning for predictive maintenance across different industries, such as manufacturing, automotive, aerospace, and energy. Finally, we discuss performance evaluation metrics and methodologies used to assess the effectiveness and reliability of predictive maintenance models, considering factors such as accuracy, precision, recall, and F1-score. Through this comprehensive exploration, this paper aims to provide insights into the practical application of machine learning for predictive maintenance and its potential impact on optimizing the performance and longevity of mechanical systems
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
In recent years, there have been many practical applications of anomaly
detection such as in predictive maintenance, detection of credit fraud, network
intrusion, and system failure. The goal of anomaly detection is to identify in
the test data anomalous behaviors that are either rare or unseen in the
training data. This is a common goal in predictive maintenance, which aims to
forecast the imminent faults of an appliance given abundant samples of normal
behaviors. Local outlier factor (LOF) is one of the state-of-the-art models
used for anomaly detection, but the predictive performance of LOF depends
greatly on the selection of hyperparameters. In this paper, we propose a novel,
heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model
that uses the proposed method shows good predictive performance in both
simulations and real data sets.Comment: 15 pages, 5 figure
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